Consistency of Privacy-Preserving Spectral Clustering under the Stochastic Block Model. - Jonathan Hehir; Aleksandra B. Slavkovic; Xiaoyue Niu (CoRR, 2021)

Perfect Spectral Clustering with Discrete Covariates. - Jonathan Hehir; Xiaoyue Niu; Aleksandra B. Slavkovic (CoRR, 2022)

Consistent Spectral Clustering of Network Block Models under Local Differential Privacy. - Jonathan Hehir; Aleksandra B. Slavkovic; Xiaoyue Niu (J. Priv. Confidentiality, 2022)

Sketch-Flip-Merge: Mergeable Sketches for Private Distinct Counting. - Jonathan Hehir; Daniel Ting; Graham Cormode (ICML, 2023)

Federated Analytics in Practice: Engineering for Privacy, Scalability and Practicality. - Harish Srinivas; Graham Cormode; Mehrdad Honarkhah; Samuel Lurye; Jonathan Hehir; Lunwen He; George Hong; Ahmed Magdy; Dzmitry Huba; Kaikai Wang; Shen Guo; Shoubhik Bhattacharya (CoRR, 2024)

Signal Recovery from Random Dot-Product Graphs under Local Differential Privacy. - Siddharth Vishwanath; Jonathan Hehir (AISTATS, 2025)

PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality. - Harish Srinivas; Graham Cormode; Mehrdad Honarkhah; Samuel Lurye; Jonathan Hehir; Lunwen He; George Hong; Ahmed Magdy; Dzmitry Huba; Kaikai Wang; Shen Guo; Shoubhik Bhattacharya (NSDI, 2025)

Interpreting Network Differential Privacy. - Jonathan Hehir; Xiaoyue Niu; Aleksandra B. Slavkovic (CoRR, 2025)